A methodology of integrating affective design with defining engineering specifications for product design

Affective design and the determination of engineering specifications are commonly conducted separately in early product design stage. Generally, designers and engineers are required to determine the settings of design attributes (for affective design) and engineering requirements (for engineering design), respectively, for new products. Some design attributes and some engineering requirements could be common. However, the settings of the design attributes and engineering requirements could be different because of the separation of the two processes. In previous studies, a methodology that considers the determination of the settings of the design attributes and engineering requirements simultaneously was not found. To bridge this gap, a methodology for considering affective design and the determination of engineering specifications of a new product simultaneously is proposed. The proposed methodology mainly involves generation of customer satisfaction models, formulation of a multi-objective optimisation model and its solving using a chaos-based NSGA-II. To illustrate and validate the proposed methodology, a case study of mobile phone design was conducted. A validation test was conducted and the test results showed that the customer satisfaction values obtained based on the proposed methodology were higher than those obtained based on the combined standalone quality function deployment and standalone affective design approach.     

Particle swarm optimization approach for multi-objective composite box-beam design

This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization (‘inspired by the choreography of a bird flock’). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design problem is to achieve (a) specified stiffness value and (b) maximum elastic coupling. The presence of maximum elastic coupling in the composite box-beam increases the aero-elastic stability of the helicopter rotor blade. The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique. The optimal geometry and ply angles are obtained for a composite box-beam design with ply angle discretizations of 10°, 15° and 45°. The performance and computational efficiency of the proposed particle swarm optimization approach is compared with various genetic algorithm based design approaches. The simulation results clearly show that the particle swarm optimization algorithm provides better solutions in terms of performance and computational time than the genetic algorithm based approaches.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

基于RIR-MOO的定制产品优化设计方法研究

大规模定制是未来产品制造的发展方向,以低成本快速响应客户的个性化需求是定制产品设计的关键。以往对定制产品设计的研究缺少从个性化需求到产品参数的流程化设计指导方法,无法从定制产品个性化的客户需求出发,快速地关联设计目标并指导产品参数的设计。为此,提出了基于RIR-MOO （relative importance ratings and multi-objective optimization,相对重要性等级-多目标优化）的定制产品优化设计方法,从客户需求出发,将关键需求映射到相关的设计目标,再通过多目标优化（multi-objective optimization,MOO）方法快速求解产品参数,实现定制产品的快速响应和优化设计。首先,针对模糊、动态变化的客户需求,利用加权区间粗糙集分析方法对客户需求进行客观分析,排除争议性大的模糊需求,同时计算不同客户需求的相对权重,得到其相对重要性等级（relative importance ratings,RIR）;然后,提出一种基于转换矩阵的客户需求与设计目标关联的方法,根据关键需求与设计目标的关联度以及关键需求的RIR,对关键需求与设计目标进行匹配,实现关键需求到设计目标的转换;最后,基于MOO方法对设计目标对应的产品参数进行优化设计,实现约束条件下定制产品的多目标优化。以汽车发动机活塞机构优化设计为例,对基于RIR-MOO的优化设计方法的可行性进行验证。结果表明,所提出的方法可以快速响应定制产品的客户需求并指导其优化设计,可为定制化产品制造企业提供指导。     

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Selection of optimal aggregate proportions is the main part of the concrete mix design optimization. Assuming circular aggregates, a new Sequential Packing Algorithm (SPA) is proposed to densify arrangement of arbitrary circles. To achieve the densest packing of circles, the porosity packed circle assemblies is optimized by a Genetic Algorithm (GA) search module. Proper adjustment of the algorithm’s parameters and selection of the initial population are effective tools for speeding up the computational progress. The model exactly solves known problems. Finally, Ideal-grading curve is presented after the implementation of GA on the data set.     

Using Genetic Algorithms to Design Experiments: A Review

Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a broad range of problems. In the last 20 years, the statistical literature has seen an increase in the use and study of this optimization algorithm for generating optimal designs in a diverse set of experimental settings. These efforts are due in part to an interest in implementing a novel methodology as well as the hope that careful application of elements of the GA framework to the unique aspects of a designed experiment problem might lead to an efficient means of finding improved or optimal designs. In this paper, we explore the merits of using this approach, some of the aspects of design that make it a unique application relative to other optimization scenarios, and discuss elements which should be considered for an effective implementation. We conclude that the current GA implementations can, but do not always, provide a competitive methodology to produce substantial gains over standard optimal design strategies. We consider both the probability of finding a globally optimal design as well as the computational efficiency of this approach. Copyright © 2014 John Wiley & Sons, Ltd.     

一种基于GA的聚类集成算法

提出了一种基于GA的聚类集成算法ECUNGA(ensemble clustering using NMI and GA).算法利用GA搜索一个与聚类集体差异度小的聚类,以此来达到综合聚类集体信息,得到更优秀的聚类的目的.算法相比于传统基于互信息理论的方法,使用GA搜索,提高了搜索的能力且具有较低计算复杂度.最后,在UCI数据集上进行实验,取得了理想的效果.     

Platform design variable identification for a product family using multi-objective particle swarm optimization

The variability of products affects customers’ satisfaction by increasing flexibility in decision-making for choosing a product based on their preferences in competitive market environments. In product family design, decision-making for determining a platform design strategy or the degree of commonality in a platform can be considered as a multidisciplinary optimization problem with respect to design variables, production cost, company’s revenue, and customers’ satisfaction. In this paper, we investigate evolutionary algorithms and module-based design approaches to identify an optimal platform strategy in a product family. The objective of this paper is to apply a multi-objective particle swarm optimization (MOPSO) approach to determine design variables for the best platform design strategy based on commonality and design variation within the product family. We describe modifications to apply the proposed MOPSO to the multi-objective problem of product family design and allow designers to evaluate varying levels of platform strategies. To demonstrate the effectiveness of the proposed approach, we use a case study involving a family of General Aviation Aircraft. We show that the proposed optimization algorithm can provide a proper solution in product family design process through experiments. The limitations of the approach and future work are also discussed.     

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.     

Parameter and tolerance design in the engineering modelling stage

Generalized parameter and tolerance design problems have been formulated as nonlinear optimization problems under a broader set of assumptions. A new approach for parameter design and tolerance design problems is outlined. This approach integrates engineering models and numerical optimization methods so it can work in the early stage of design where a good engineering model is available to simulate the real product or process. The new approach is also able to handle multiple quality characteristics and constraints. Several important theoretical results have been derived by the authors for tolerance design problems that could serve as guidelines for optimal tolerance design and tolerance distribution.     

Online design reuse tool for the support of the generation,embodiment and detailed design of products

Design reuse is becoming a widely accepted strategy to meet the increasingly fierce competition and highly diversified customer needs in product development. In this research, an integrated and systematic framework to computerize the design reuse process was formulated and a prototype online design reuse system was implemented. This system embodies the evolutionary process of design reuse and supports design reuse from the original requirements through the concept and embodiment stage to achieve a final detailed design. The prototype system retrieves products using multi-indices and a stepwise winnowing method. The advanced-search module of the system solves a design problem using two intelligent search methods to retrieve reusable products. Finally, the system can adapt these retrieved products to generate a new design. The design reuse framework is based on related design methodologies such as function taxonomy, product family, etc. Genetic algorithms and case-based reasoning are adopted in this system to achieve an intelligent search. The variant design method is used in detailed design reuse.     

Intelligent layout planning for rapid prototyping

Significant savings in cost and time can be achieved in rapid prototyping (RP) by manufacturing multiple parts in a single setup to achieve efficient machine volume utilization. This paper reports the design and implementation of a system for the optimal layout planning of 3D parts for a RP process. A genetic algorithm (GA) based search strategy has been used to arrive at a good packing layout for a chosen set of parts and RP process. A two stage approach has been proposed to initially short-list acceptable orientations for each part followed by the search for a layout plan which optimizes in terms of final product quality and build time. The GA uses a hybrid objective function comprising of the weighted measures like part build height, staircase effect, volume and area-of-contact of support structures. In essence it captures the key metrics of efficiency and goodness of packing for RP. The final layout plan is produced in the form of a composite part CAD model which can be directly exported to a RP machine for manufacturing. Design methodology of the system has been presented with some representative case studies.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

A New Approach for Selection of Optimal Process Parameters in Abrasive Water Jet Cutting

This paper presents a new approach, based on the principles of fuzzy logic and Genetic Algorithm (GA) for selection of optimal process parameters in Abrasive Water Jet (AWJ) cutting of granite to any predetermined depth, using multi-criteria optimization technique. The proposed approach suggests the best combination of process parameters such as water jet pressure, jet traverse rate and abrasive flow rate for cutting granite material to any predetermined depth. GA, in combination with the model built based on fuzzy approach, generates several sets of process parameters satisfying the objective of achieving the desired depth of cut. These sets of parameters are subjected to multi-criteria optimization procedure which suggests a set of process parameters that can minimize the cost of production by increasing the rate of production and reducing the consumption of abrasives, maintaining the desired depth of cut within the specified limits. The proposed approach is validated with suitable experiments conducted on Paradiso granite.     

DISCRETE MANUFACTURING PROCESS DESIGN OPTIMIZATION USING COMPUTER SIMULATION AND GENERALIZED HILL CLIMBING ALGORITHMS

Discrete manufacturing process designs can be modelled using computer simulation. Determining optimal designs using such models is very difficult, due to the large number of manufacturing process sequences and associated parameter settings that exist. This has forced researchers to develop heuristic strategies to address such design problems. This paper introduces a new general heuristic strategy for discrete manufacturing process design optimization, called generalised hill climbing (GHC) algorithms. GHC algorithms provide a unifying approach for addressing such problems in particular, and intractable discrete optimization problems in general. Heuristic strategies such as simulated annealing, threshold accepting, Monte Carlo search, local search, and tabu search (among others) can all he formulated as GHC algorithms. Computational results are reported with various GHC algorithms applied to computer simulation models of discrete manufacturing process designs under study at the Materials Process Design Branch of Wright Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA).     

创新扩散视野下颈椎枕的产品设计策略研究

目的 针对现有产品设计在功能创新与产品采纳等方面的问题,基于创新扩散理论,以产品的功能、观念等方面及迭代策略方法为研究对象,创造和优化颈椎枕产品设计方法。方法 以智能助眠颈椎枕产品设计为实验样本,从创新扩散理论视角归纳产品创新过程中的核心问题,将产品的设计及优化方向纳入核心功能、用户体验、应用场景创新等不同优化路径,并在用户群体中划分出具有强扩散倾向的用户群体,根据其需求制定不同迭代策略,从而获得不同优化策略的细分迭代产品并投放市场进行扩散检验。结果 将2类不同产品线的迭代产品进行交叉对比验证,发现了总采纳比原产品提升了15.8%。同时对潜在用户群体进行针对性扩散实验结果,显示出显著的扩散潜量边界突破的表达。结论 探索了以创新扩散为目标的设计思路,在颈椎枕产品优化设计中的应用。提出了“扩散特征表达需求,并驱动创新,而后影响创新采纳效果”的产品设计模式。证明了根据扩散过程的信息反馈进行的产品迭代,提升了用户的新增采纳概率和模仿采纳概率。同时验证了以创新扩散需求为导向的产品设计策略研究的可行性。     

Optimal design of gating systems by gradient search methods

A numerical optimization technique based on gradient-search is applied to obtain an optimal design of a typical gating system used for the gravity process to produce aluminum parts. This represents a novel application of coupling nonlinear optimization techniques with a foundry process simulator, and it is motivated by the fact that a scientifically guided search for better designs based on techniques that take into account the mathematical structure of the problem is preferred to commonly found trial-and-error approaches. The simulator applies the finite volume method and the VOF algorithm for CFD analysis. The direct gradient optimization algorithm, sequential quadratic programming (SQP), was used to solve both a 2D and a 3D gating system design problems using two design variables. The results clearly show the effectiveness of the proposed approach for finding high quality castings when compared with current industry practices.     

Optimization of machining datum selection and machining tolerance allocation with genetic algorithms

In machining process planning, selection of machining datum and allocation of machining tolerances are crucial as they directly affect the part quality and machining efficiency. This study explores the feasibility to build a mathematical model for computer aided process planning (CAPP) to find the optimal machining datum set and machining tolerances simultaneously for rotational parts. Tolerance chart and an efficient dimension chain tracing method are utilized to establish the relationship between machining datums and tolerances. A mixed-discrete nonlinear optimization model is formulated with the manufacturing cost as the objective function and blueprint tolerances and machine tool capabilities as constraints. A directed random search method, genetic algorithm (GA), is used to find optimum solutions. The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances. The proposed model and solution procedure can be used as a building block for computer automated process planning.     

Modular product family design: Agent-based Pareto-optimization and quality loss function-based post-optimal analysis

The advent of mass customization and increased manufacturing competition has necessitated that many companies offer platform-oriented multiple product variants. Various design strategies such as Design for Variety and product family design have become critical in this respect. This paper provides a two-step approach to tackle the modular product family design problem. The first step performs a multi-objective optimization using a multi-agent framework to determine the Pareto-design solutions for a given module set. The proposed multi-agent framework is new and has built in flexibility to handle various constraints such as module compatibility during the optimization process. The second step performs post-optimization analysis that includes a novel application of the quality loss function to determine the optimal platform level for a related set of product families and their variants. The proposed method is applied to a product family design example to demonstrate its validity and effectiveness.     

Die shape optimal design in three-dimensional shape metal extrusion by the finite element method

A new approach to die shape optimal design in shape extrusion is presented. In this approach, the design problem is formulated as an optimization problem incorporating the three-dimensional finite element analysis model, and optimization of the die shape is conducted on the basis of the design sensitivities. The approach is applied to the determination of the die shapes for extrusion of parts with various cross sections including polygons and T sections. © 1998 John Wiley & Sons, Ltd.